AI 创作日记 | DeepSeek 智能补货算法升级，融合运筹学与深度学习降低缺货率

Python
插入到 IDE复制新建文件保存到本地Applybusiness_constraints = {
    'warehouse_capacity': 20000,  # 立体仓库容量（单位：标准箱）
    'cold_chain_ratio': 0.3,      # 冷链商品占比（30%）
    'promotion_frequency': 2.5,   # 月均促销次数（2.5次/月） 
    'lead_time': {
        'normal': 48,  # 常规补货周期(小时)
        'urgent': 6    # 紧急补货周期（小时）
    }
}

# 立体仓库三维空间优化示例
def optimize_3d_storage(items):
    """
    基于商品周转率的三维存储优化
    输入：items(List[Item]) - 商品对象列表，含属性：
         volume: 体积, turnover_rate: 周转率, weight: 重量
    输出：storage_plan(Dict) - 货架分配方案
    """
    # 按周转率降序排序（高频商品靠近出口）
    sorted_items = sorted(items, 
        key=lambda x: x.turnover_rate, reverse=True)
    
    # 三维装箱算法（考虑承重平衡）
    storage = {}
    z_level = 0
    current_weight = 0
    
    for idx, item in enumerate(sorted_items):
        # 每层承重不超过2000kg
        if current_weight + item.weight > 2000:
            z_level += 1
            current_weight = 0
            
        storage[item.id] = {
            'position': (idx%10, idx//10%10, z_level),  # (x,y,z)
            'weight': item.weight
        }
        current_weight += item.weight
    
    return storage

class ColdChainOptimizer:
    def __init__(self):
        self.temp_zones = {
            'frozen': (-18, 50),   # (温度℃, 容量m³)
            'chilled': (4, 100),
            'ambient': (15, 150)
        }
    
    def allocate_space(self, products):
        """
        冷链商品存储分配
        考虑因素：保质期、到货时间、出库频次
        """
        allocation = {}
        for product in products:
            # 按保质期倒排（短保商品优先分配易取货位）
            if product.expiry_days <= 3:
                loc = self._find_urgent_position(product.temp_type)
            else:
                loc = self._find_regular_position(product.temp_type)
            
            allocation[product.id] = loc
            self._update_inventory(loc, product.volume)
        
        return allocation
    
    def _find_urgent_position(self, temp_type):
        """获取靠近出口的货位"""
        # 实现逻辑省略
        pass

def demand_forecast_with_promo(history_data, promo_plan):
    """
    融合促销计划的需求预测
    输入：
        history_data (DataFrame) - 历史销售数据
        promo_plan (Dict) - 促销计划{
            'start_date': '2023-12-12',
            'discount_rate': 0.3, 
            'bundled_skus': [123,456]
        }
    输出：预测销量
    """
    # 特征工程
    features = []
    for window in [7, 30, 90]:  # 多个时间窗口
        features.append(history_data.rolling(window).mean())
    
    # 促销影响因子计算
    promo_impact = calculate_promo_impact(
        promo_plan['discount_rate'],
        len(promo_plan['bundled_skus'])
    )
    
    # LSTM模型预测（考虑促销因子）
    model = LSTMForecaster(input_size=len(features)+1)
    prediction = model.predict(features + [promo_impact])
    
    return prediction

class DualModeReplenishment:
    def __init__(self):
        self.normal_cost = 0.8  # 元/件·公里
        self.urgent_cost = 2.5  # 元/件·公里
    
    def optimize(self, demand_pred, inventory):
        shortage = demand_pred - inventory
        if shortage <= 0:
            return None
        
        # 成本最优解计算
        normal_cost = self._calc_normal_cost(shortage)
        urgent_cost = self._calc_urgent_cost(shortage)
        
        # 选择最优模式
        if urgent_cost < normal_cost * 1.2:  # 允许20%成本溢价
            return {'mode': 'urgent', 'qty': shortage}
        else:
            return {'mode': 'normal', 'qty': shortage}
    
    def _calc_normal_cost(self, qty):
        # 包含仓储、运输、人力等成本
        return qty * self.normal_cost * distance
    
    def _calc_urgent_cost(self, qty):
        # 包含加急费、优先配送费等
        return qty * self.urgent_cost * distance

class DataIntegrator:
    def __init__(self):
        self.sources = ['POS系统', '仓储WMS', '天气API', '竞品爬虫']
        
    def process_stream(self):
        # 流式数据特征对齐
        window_spec = Window.partitionBy('sku_id').orderBy('timestamp').rowsBetween(-24, 0)
        df = df.withColumn('rolling_avg', F.avg('sales').over(window_spec))
        
        # 实时维度关联
        join_cond = [df['store_id'] == inventory_df['store_id']]
        df = df.join(inventory_df, join_cond, 'left').fillna(0)

class TemporalFusionTransformer(nn.Module):
    def __init__(self, hidden_size=64):
        super().__init__()
        self.encoder = LSTMEncoder(hidden_size)
        self.decoder = TransformerDecoder(hidden_size)
        self.attention = MultiheadAttention(hidden_size, 4)
        
    def forward(self, x):
        # 时间模式抽取
        seq_features = self.encoder(x['temporal'])
        
        # 静态特征融合
        static_emb = self.embedding(x['static'])
        
        # 多维度注意力
        context = self.attention(seq_features, static_emb)
        return self.decoder(context)

from docplex.mp.model import Model

mdl = Model(name='inventory_optimization')

# 决策变量
order_amount = mdl.integer_var_matrix(skus, periods, name='order')
safety_stock = mdl.continuous_var_dict(skus, name='safety')

# 目标函数
mdl.minimize(
    mdl.sum(holding_cost[s]*inventory[s,t] + 
           shortage_cost[s]*shortage[s,t] 
           for s in skus for t in periods)
)

# 鲁棒性约束
for s in skus:
    mdl.add_constraints(
        inventory[s,t] >= safety_stock[s] * (1 + demand_uncertainty[s,t]),
        for t in periods
    )

def solve_parallel(model, n_workers=8):
    with mp.Pool(n_workers) as pool:
        # 场景树分解
        scenario_batches = np.array_split(scenarios, n_workers)
        results = pool.map(solve_scenario, scenario_batches)
        
    return aggregate_solutions(results)

class RealTimeEngine:
  def __init__(self):
    self.kafka_consumer = KafkaConsumer('inventory_updates')
    self.model = load_model('or_dl_model.pkl')
    self.executor = ERPConnector()

  def start(self):
    while True:
      # 实时获取库存状态
      message = self.kafka_consumer.poll()
      current_inv = parse_message(message)
      # 生成补货决策
      demand_pred = self.model.predict(current_inv)
      order_plan = self.model.optimize(demand_pred)
      # 执行补货指令
      self.executor.submit_order(order_plan)